Homework 1 - R Markdown

Loading the data

Before we can analyze anything, we must load our data. Since we do not have a CSV, we can use the read.table() function to ensure accurate importing.

ClimateData <- read.table("clim.txt", header = T)
# load climate dataset with a header

Once our data is loaded, we see that this dataset gives us:

• Minimum temperatures;

• Maximum temperatures; and

• Precipitation

for every day of the year since 1942.

Annual Trends

Next, we want to explore the general trend in precipitation and temperature throughout the year. We can do this by aggregating those variables by month and year with the aggregate() function.

The following code will take our daily data and find the average monthly precipitation for each year:

precip <- aggregate(x = ClimateData$rain, by = list(ClimateData$month, ClimateData$year), FUN = mean)  
# aggregate mean monthly precipitation

colnames(precip) <- c("Month", "Year", "Precip")  
# rename columns

We can use a similar process to get average monthly temperatures. However, since we are given minimum and maximum temperatures, we must take their mean to get the average.

tmax <- aggregate(x = ClimateData$tmax, by = list(ClimateData$month, ClimateData$year), FUN = mean)  
# aggregate mean monthly maximum temperatures

colnames(tmax) <- c("Month", "Year", "TMax")  
# rename columns

tmin <- aggregate(x = ClimateData$tmin, by = list(ClimateData$month, ClimateData$year), FUN = mean)  
# aggregate mean monthly minimum temperatures

colnames(tmin) <- c("Month", "Year", "TMin") 
# rename columns

tavg <- (tmax$TMax + tmin$TMin)/2  
# calculate overall mean monthly temperatures by averaging monthly maximum and minimum temperatures

dates <- tmax[,-3]  
# borrow the month and year values from our maximum temperatures' data frame since they aren't copied automatically 

tavg <- cbind(dates, tavg)  
# bind the dates to our average temperature values 

After aggregating the data to get monthly precipitation and average temperatures, we can use this code to generate the following graphs:

boxplot(precip$Precip ~ precip$Month, xlab = "Month", ylab = "Rainfall (inches)", main = "Average Monthly Precipitation, 1942-2016")

boxplot(tavg$tavg ~ tavg$Month, xlab = "Month", ylab = "Temperature (degrees)", main = "Average Monthly Temperature, 1942-2016")

To find the wettest and driest years overall, we can again aggregate our data, this time only by year. Then, we can use the which.max() and which.min() functions to pick out the years with the highest and lowest average annual precipitation.

yearly_precip <- aggregate(x = precip$Precip, by = list(precip$Year), FUN = mean) 
# aggregate mean yearly precipitation 

colnames(yearly_precip) = c("Year", "Precip")  
# rename columns

wet_year <- which.max(yearly_precip$Precip)  
# pick out highest value

wet_year  
# display highest value

dry_year <- which.min(yearly_precip$Precip)  
# pick out lowest value 

dry_year  
# display lowest value 

When we go to the rows output by those functions, we see that our wettest year was 1982 with an annual average of 5.85 inches, and our driest year was 2013 with an annual average of 0.72 inches.

Such drastic variations in rainfall can have severe consequences on ecosystems. Many species of native vegetation such as the coast live oak (Quercus agrifolia) can be damaged by drought and take years to recover.

The following images illustrate the dramatic difference between a wet and dry year in the California oak woodlands.

Seasonal Trends

We can also explore trends in precipitation and average temperature by season. Although our dataset did not come with a column identifying seasons, we can easily create one with the following code.

precip$Season <- ""  
# create a new column in our precipitation data frame

precip[precip$Month %in% c(4:6),"Season"] <- 1  
# assign 1 for months 4-6 (spring)

precip[precip$Month %in% c(7:9),"Season"] <- 2  
# assign 2 for months 7-9 (summer)

precip[precip$Month %in% c(10:12),"Season"] <- 3  
# assign 3 for months 10-12 (fall)

precip[precip$Month %in% c(1:3),"Season"] <- 4  
# assign 4 for months 1-3 (winter) 

We want to repeat the same process for average temperatures. However, there is another option for doing so that requires even less code, using the ifelse() function.

tavg$Season <- ""  
# create a new column in our average temperature data frame

tavg$Season <- ifelse(tavg$Month %in% c(1,2,3), "4", ifelse(tavg$Month %in% c(4,5,6), "1", ifelse(tavg$Month %in% c(7,8,9), "2", "3")))  
# assign 4 for months 1-3 (winter), 1 for months 4-6 (spring), 2 for months 7-9 (summer), and 3 for everything else, i.e. months 10-12 (fall)

Once our data has all been given a new value for “Season”, we can use it to find the wettest and driest seasons. Once again, we will use the aggregate() function. First, we can find the seasonal averages for each year. Then, we can repeat the process to find the overall seasonal averages for all years.

seasonal_precip <- aggregate(x = precip$Precip, by = list(precip$Season, precip$Year), FUN = mean)  
# aggregate average seasonal precipitation for each year

colnames(seasonal_precip) = c("Season", "Year", "Precip")  
# rename columns

seasonal_temp <- aggregate(x = tavg$tavg, by = list(tavg$Season, tavg$Year), FUN = mean)  
# aggregate average seasonal temperatures for each year

colnames(seasonal_temp) = c("Season", "Year", "Temp")  
# rename columns 

avg_seasonal_precip <- aggregate(seasonal_precip$Precip, by = list(seasonal_precip$Season), FUN = mean)  
# aggregate average seasonal temperatures over all years 

colnames(avg_seasonal_precip) = c("Season", "Precip")  
# rename columns

Now, we can use which.max() and which.min() again to identify the seasons with the highest and lowest precipitation. (Or, since there are only four values, we could just look at them and see for ourselves.)

wet_season <- which.max(avg_seasonal_precip$Precip)
# pick out highest value 

wet_season
# display highest value

dry_season <- which.min(avg_seasonal_precip$Precip)
# pick out lowest value 

dry_season
# display lowest value 

Either way, we can see that the wettest season is winter with an average precipitation of 5.85 inches over all years, and the driest season is summer with an average precipitation of 0.29 inches over all years.

Winter Precipitation and Summer Temperatures

Now that we have datasets containing average seasonal precipitation and temperatures, we can use them to explore additional seasonal trends. Two seasonal variables of particular interest are winter precipitation and summer temperatures. These variables in particular are the limiting factors for many varieties of plant species.

To pick out only the winter precipitation and summer temperature data from our full datasets, we can use the subset() function.

winter_precip <- subset(seasonal_precip, Season == 4)  
# create a new dataset with only winter precipitation data for each year

winter_precip <- winter_precip[,-1]  
# remove the "season" column since we no longer need it 

summer_temp <- subset(seasonal_temp, Season == 2)  
# create a new dataset with only summer temperature data for each year 

summer_temp <- summer_temp[,-1]  
# remove the "season" column since we no longer need it 

Once we have subsetted our data so that only our variables of interest remain, we can plot them on the same graph to compare trends.

plot(x = winter_precip$Year, y = winter_precip$Precip, type = "l", col = "cyan", xlab = "Year", ylab = "Value", main = "Winter Precipitation and Summer Temperature")
lines(x = summer_temp$Year, y = summer_temp$Temp, col = "purple")

As this graph illustrates, both winter precipitation (shown here in blue) and summer temperatures (shown here in purple) can vary greatly over the years. However, it appears that drier winters are often associated with hotter summers, creating a double-whammy for sensitive vegetation. Keeping track of these trends can help us to remain aware of when ecosystems are particularly vulnerable due to climatic variations. new change2

P.S. Here is a change to be committed.